ITMO
ru/ ru

ISSN: 1023-5086

ru/

ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

Article submission Подать статью
Больше информации Back

DOI: 10.17586/1023-5086-2021-88-12-50-58

УДК: 621.397

Detection and tracking of weakly emitting point objects based on the analysis of a sequence of miniseries of images

For Russian citation (Opticheskii Zhurnal):

Меденников П.А., Павлов Н.И. Обнаружение и сопровождение точечных слабоизлучающих объектов на основе анализа последовательности минисерий изображений // Оптический журнал. 2021. Т. 88. № 12. С. 50–58. http://doi.org/10.17586/1023-5086-2021-88-12-50-58

 

Medennikov P.A., Pavlov N.I. Detection and tracking of weakly emitting point objects based on the analysis of a sequence of miniseries of images [in Russian] // Opticheskii Zhurnal. 2021. V. 88. № 12. P. 50–58. http://doi.org/10.17586/1023-5086-2021-88-12-50-58

For citation (Journal of Optical Technology):

P. A. Medennikov and N. I. Pavlov, "Detection and tracking of weakly emitting point objects based on the analysis of a sequence of miniseries of images," Journal of Optical Technology. 88(12), 716-721 (2021). https://doi.org/10.1364/JOT.88.000716

Abstract:

This paper discusses the problem of detecting and tracking moving, weakly emitting point objects. It is proposed to solve it by using a sequential two-stage method of processing miniseries of images (frames) obtained by an optoelectronic system. The proposed method includes an original procedure of interframe processing of miniseries of images, a feature of which is to operate with two lists of objects: the main list and an intermediate (buffer) list. The indicated lists are updated after each miniseries of images is processed. The buffer list of objects in this case plays a subsidiary role and is used to filter out (eliminate) the false signals that appear during frame processing of the images. The use of the proposed interframe processing procedure makes it possible to work with low-SNR images, and this is regarded as a substantial advantage of the well-known track-before-detect method. Statistical experiments using miniseries of model frames confirm that the proposed method is workable.

Keywords:

detection and tracking of weakly emitting point objects, interframe processing of images, track-before-detect method, sequential (two-stage) method of image processing, sequence of miniseries of images, buffer list

OCIS codes: 100.4999

References:

1. D. B. Reid, “An algorithm for tracking multiple targets,” IEEE Trans. Autom. Control 24(6), 843–854 (1979).
2. Y. Bar-Shalom and T. E. Fortman, Tracking and Data Association (Academic Press, New York, 1988).
3. C. Hue, J.-P. Le Cadre, and P. Perez, “Tracking multiple objects with particle filtering,” IEEE Trans. Aerosp. Electron. Syst. 38(3), 791–812 (2002).
4. R. P. S. Mahler, “Multitarget Bayes filtering via first-order multi-target moments,” IEEE Trans. Aerosp. Electron. Syst. 39(4), 1152–1178 (2003).
5. B.-N. Vo and W.-K. Ma, “The Gaussian mixture probability hypothesis density filter,” IEEE Trans. Audio Speech Lang. Process. 54(11), 4091–4104 (2006).
6. A. E. Kolessa, “Correction of point target track parameters by means of joint processing of sequential images” January 2009, www.researchgate.net/publication/282857725.
7. V. M. Artem’ev, A. O. Naumov, and L. L. Kokhan, “Detection of point objects on images under conditions of indeterminacy,” Informatika (2), 15–24 (2010).
8. V. T. Fisenko, V. I. Mozheko, T. Yu. Fisenko, L. D. Vilesov, and D. A. Fedorov, “Method of automatically detecting and tracking many small objects under conditions of a priori indeterminacy,” Izv. Vyssh. Uchebn. Zaved. Prib. 57(10), 17–22 (2014).
9. D. K. Barton, Modern Radar System Analysis (Artech House Inc., Norwood, MA, 1988).
10. R. H. Kingston, Detection of Optical and Infrared Radiation (Springer-Verlag Berlin, Heidelberg, 1978).
11. N. R. Berenkov and A. G. Tartakovski, “Effective algorithms for distinguishing weakly distinguishable tracks of space objects,” Tr. Mosk. Fiz.-Tekhnich. Inst. 12(2), 5–20 (2020).
12. Y. Barniv, “Dynamic programming solution for detecting dim moving targets,” IEEE Trans. Aerosp. Electron. Syst. AES-21(3), 144–156 (1985).
13. Y. Barniv and O. Kella, “Dynamic programming solution for detecting dim moving targets. Part II: Analysis,” IEEE Trans. Aerosp. Electron. Syst. AES-23(6), 776–788 (1987).
14. E. S. M. Gang-Wang and R. M. Inigo, “A pipeline algorithm for detection and tracking of pixel-sized target trajectories,” Proc. SPIE 1305, 167–177 (1990).
15. I. S. Reed, R. M. Gagliardi, and L. V. Stott, “A recursive moving-target-indication algorithm for optical image sequences,” IEEE Trans. Aerosp. Electron. Syst. 26(3), 434–440 (1990).
16. H. Hu, Z. Jing, and S. Hu, “Track before detect for point targets with particle filter in infrared image sequences,” Chin. Opt. Lett. 3(6), 322–325 (2005).
17. O. Nichtern and S. R. Rotman, “Parameter adjustment for a dynamic programming track-before-detect-based target detection algorithm,” EURASIP J. Adv. Signal Process. 2008, 146925 (2008).
18. H. Cho and J. Chun, “A new TBD-DP algorithm using multiple IR sensors to locate the target launch point,” Proc. SPIE 8185, 81850P (2015).
19. H. Yang, “Detection and tracking of infrared dim small image sequence moving target,” Open Autom. Control Syst. J. 7, 1698–1704 (2015).
20. B. Zhao, S. Xiao, H. Lu, and J. Liu, “Point target detection in space-based infrared imaging system based on multi-direction filtering fusion,” Prog. Electromagn. Res. M 56, 145–156 (2017).
21. N. Ito and S. Godsill, “A multi-target track-before-detect particle filter using superpositional data in non-Gaussian noise,” arXiv:2003.05778v2[eess.SP] (2020).
22. P. A. Medennikov, “Algorithm for detecting and determining the coordinates of a point object,” J. Opt. Technol. 86(8), 510–514 (2019) [Opt. Zh. 86(8), 65–69 (2019)].
23. A. A. Abakumova, T. P. Malinova, P. A. Medennikov, and N. I. Pavlov, “Algorithmic simulation-modeling software complex for the investigation and development of optoelectronic observation systems,” J. Opt. Technol. 86(8), 503–509 (2019) [Opt. Zh. 86(8), 56–64 (2019)].
24. A. S. Tsvetkov, Handbook for Practical Work with the Hipparcos Catalog (Sankt-Peterburgski Universitet, St. Petersburg, 2005).